Software Development Lifecycle In Engineering

Software development is quietly undergoing its biggest shift in decades. Not because of new frameworks. Not because of faster cloud. But because agents are entering the SDLC. Traditional development follows a slow, sequential loop: requirements → design → coding → testing → reviews → deployment → monitoring → feedback. Each step depends on human handoffs, manual fixes, delayed feedback, and long iteration cycles—often stretching from weeks to months. Agentic coding changes this entirely. Instead of humans writing everything line-by-line, developers express intent. Agents understand requirements, implement features, generate tests and documentation, deploy changes, monitor production, and even propose fixes. The lifecycle compresses from weeks and months into hours or days. Here’s what actually changes: • Sequential handoffs become continuous agent-driven flows • Humans shift from coding to guiding and reviewing • Documentation is generated inline, not after delivery • Testing happens automatically alongside implementation • Incidents trigger agent-assisted remediation • Monitoring feeds directly back into learning loops • Iteration becomes constant, not episodic In the Agentic SDLC: You describe outcomes. Agents execute workflows. Humans validate critical decisions. Systems learn continuously. The result isn’t just faster delivery. It’s a fundamentally different operating model for engineering—where feedback is immediate, fixes are automated, and improvement never stops. This is how software teams move from manual development pipelines to self-improving delivery systems.

Most of us review code in the wrong order. We spot a missing test or a style inconsistency before even asking whether the code is correct. We should think about it differently. The first question should always be: Does this code do what it is supposed to do? If the answer is no, nothing else matters. Style, structure, tests - all secondary to correctness. Once you are confident it is correct, ask if it is clear. Can someone else (or you, six months from now) understand what is happening and why? Clarity in code helps ensure it does not become a liability. Then check whether it matches the style and conventions, because inconsistencies add cognitive load for everyone who reads the codebase afterward. After that, look for duplication. Is this solving a problem that is already solved somewhere else? Could this be a shared utility? Finally, ask whether it is well tested. Not just "are there tests" (non-sensical ones), but do the tests actually cover the meaningful cases? Correctness. Clarity. Style. Deduplication. Tests. In that order, every time. Hope this helps.

AI systems become risky when there are no guardrails controlling how they behave at scale. Over the years, I’ve seen teams rush into building AI capabilities— but very few spend enough time designing the systems that keep AI safe, reliable, and accountable. That’s where AI Governance & Security comes in. Think of this as the foundation layer for enterprise AI systems 👇 🔹 Identity & Access Control RBAC, ABAC, IAM, MFA, SSO—control who can access what, and under which conditions. 🔹 Data Protection Encryption, tokenization, masking, secure pipelines—protect sensitive data across its lifecycle. 🔹 Risk Management Risk scoring, bias detection, hallucination monitoring, threat intelligence—identify and reduce AI risks early. 🔹 Monitoring & Observability Real-time tracking, anomaly detection, logging—understand how your AI behaves in production. 🔹 Audit & Accountability Traceability, audit logs, documentation—ensure every decision can be reviewed and explained. 🔹 Compliance & Governance GDPR, EU AI Act, ISO 42001—align AI systems with regulatory and ethical standards. 🔹 Human Oversight HITL, approvals, escalation workflows—keep humans in control for critical decisions. A few critical patterns I’ve seen work in real systems: ✔ Define ownership of AI decisions (RESP) ✔ Enforce policies, don’t just document them ✔ Continuously monitor drift, bias, and anomalies ✔ Always maintain traceability across data and decisions ✔ Introduce human checkpoints for high-risk actions The biggest mistake? Treating AI governance as a compliance checkbox. It’s not. It’s what separates experimental AI systems from enterprise-grade, production-ready AI systems. Because in AI… it’s not just about what the model can do. It’s about how safely, reliably, and responsibly it does it at scale. Follow Vaibhav Aggarwal for more such insights!!

The PM role just split in two. One group ships daily. The other ships quarterly. Same title, completely different job. The quarterly cadence made sense when building was expensive. You wrote the spec, design interpreted it, eng interpreted that, everyone waited for the sprint. Every handoff cost a week. When a prototype takes 45 minutes instead of six weeks, that whole chain collapses, and the two groups stop looking anything alike. The daily group did one thing differently. They handed the part of the job that used to be their edge to an agent. A PM's old alpha was consuming more user feedback than anyone else on the team. Reading every support thread, every issue, every release note, then ranking what mattered. An agent does all of that now. It pulls the discussions, the issues, the releases, scores each one by priority, grades its own accuracy, and feeds the corrections back into itself overnight. I watched the CPO of Arize build exactly this loop live. Empty directory, four plain-English asks in a terminal, a working PM agent in under 45 minutes. On its first pass it caught its own blind spot: feature requests were outscoring production bugs. That kind of scoring drift normally takes weeks to notice in a manual backlog. Here's what makes it compound. She told the agent where its scoring was wrong. Her judgment refined the eval. The refined eval improved the agent. The better agent produced cleaner traces. Cleaner traces sharpened the next eval. The machine consumes the feedback. The PM defines what "good" means and corrects it when it drifts. Taste is the output. The eval is where you put it. So the daily side looks like this: issue comes in, the PM catches it through the agent, Claude Code prototypes a fix, it ships that afternoon. The bottleneck moved from "find the problem" to "decide if it matters." That second call is still entirely the PM's. The daily side is a setup, not a club. You build into it. 1. The self-improving PM agent, full build: https://lnkd.in/gPSEUCH8 2. AI evals for PMs, from scratch: https://lnkd.in/eGbzWMxf 3. Evals are the new PRD (with Braintrust's CEO): https://lnkd.in/gCK_RpFW 4. AI agents for PMs, the ground-up guide: https://lnkd.in/eeey5Cxr 5. Claude Code for PMs (video): https://lnkd.in/eUyPEAma Start with one agent, one scoring rule, one daily check. The split looks permanent from the quarterly side. From the daily side it's just a workflow you set up one Tuesday.

A viral image of an ATM in Ludhiana recently caught my attention - a dangerously steep ramp ending abruptly at a glass door, with a staircase running alongside that leads nowhere. A perfect reminder of a hard-earned lesson in fintech: "Compliance isn’t just a checkbox." Product Managers: You don't want to miss saving 💾 this post for your future reference. This ramp was technically "compliant" - yes, there was a wheelchair access ramp. But it completely missed the purpose of accessibility. People had angry comments on social media about the apathy with which wheelchair-bound customers were treated and how the bank had made a mockery of accessibility. No amount of regulation can account for 'compliance as a checkbox' implementations that are designed to meet the regulation but not serve their intended purpose. It's the same trap I've seen countless fintech products fall into - implementing regulations as mere checkboxes rather than embracing them as design principles. I've experienced regulatory hurdles umpteen times in product launches; in fact, I've never experienced a straightforward implementation that hasn't hit a regulatory roadblock. BUT I can say this confidently: Compliance-first design is the secret sauce that makes the battle easier and less arduous, and inarguably 'faster' IF You just stick to the first principles of building this into your product strategy from day one . Regulations can either slow you down or become your competitive edge. To make compliance your strategic advantage, here's my 3-step playbook: 1/ Design Integration: Make regulatory adherence a natural part of the user experience rather than an afterthought ↳Embed compliance requirements into your initial product design ↳Get feedback from legal and compliance teams, and even the regulator if needed ↳Validate, Test, Iterate, Repeat 2/ Cross-Functional Collaboration: Build bridges between product, legal/compliance teams from day one ↳Involve them early ↳Make compliance & legal stakeholders brainstorm and provide feedback ↳Balance innovation with regulatory requirements using case studies and data to back up assertions instead of getting into crosshairs with them 3/ Validate Early, Validate Often: ↳Test with real scenarios ↳Get early feedback from regulators ↳Regular compliance assessments, no matter what stage of development you are in One golden tip - document everything, err on the side of caution when it comes to building and fostering trust with legal and compliance counterparts. The lesson in one line? Build WITH compliance, not around it. Instead of working around regulations, let's build with them. Because when you design within the right guardrails, innovation doesn't just survive—it scales. What's your strategy for managing fintech compliance? Share below. 👍 LIKE this post, 🔄 REPOST this to your network and follow me, Monica Jasuja

𝐀𝐈 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 & 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐋𝐚𝐰𝐬 𝐟𝐨𝐫 𝐆𝐞𝐧𝐀𝐈 𝐀𝐩𝐩𝐬 Building GenAI Apps for a Global Audience?  Understanding Regional Data Protection and AI laws is not optional, it is foundational. Here is what you need to know: 1. UNDERSTANDING GLOBAL REGULATORY VARIANCE Building GenAI for a global audience requires understanding regional data protection and AI laws. Key Regulations by Region: • EU AI Act: Risk-based AI obligations for certain AI systems and transparency use cases • GDPR (EU): Transparency & Consent • DPDP (India): Digital Personal Data Protection • PIPL (China): Strict Data Localization • CCPA (California): Data Access & Opt-Out • LGPD (Brazil): Local Compliance Rules 2. IMPACT OF THESE REGULATIONS ON YOUR AI TRAINING DATA To build compliant GenAI apps,  Ensure that data used for training AI models follows the regional rules: Data Collection → Processing → Model Training → Deployment Three Core Requirements: a. User Consent: Obtain explicit consent for data collection and use b. Data Minimization: Collect only necessary data for the intended purpose c. Anonymization: Remove personally identifiable information from training data 3. MITIGATING AI ETHICS AND BIAS RISKS AI systems must be fair and ethical, particularly in high-risk areas: a. Fairness: Ensure your AI models don't discriminate, especially in areas like recruitment or finance. b. Bias Mitigation: Regularly test and adjust your models to reduce bias in the outputs. 4. ENSURING TRANSPARENCY IN AI MODEL DEVELOPMENT Transparency is a cornerstone of compliance, especially when your AI impacts users directly: a. Explainability: Protect data in transit and at rest. b. Consent Management: Collect, track, and manage user consent. c. Privacy by Design: Embed privacy into every system layer. 5. MANAGING CROSS-BORDER DATA FLOW GenAI apps often rely on data from various regions, so it's critical to understand data sovereignty laws: a. Data Sovereignty: Follow local laws on where data is stored and processed. b. Data Transfer Agreements: Use SCCs or BCRs for compliant cross-border transfers. THE COMPLIANCE CHECKLIST Before launching GenAI globally, verify: 1. Regional Compliance: • GDPR for EU? (Transparency & Consent) • DPDP for India? (Data Protection) • PIPL for China? (Data Localization) • CCPA for California? (Access & Opt-Out) • LGPD for Brazil? (Local Rules) 2. Training Data: • User consent obtained? • Data minimized? • PII anonymized? 3. Ethics & Bias: • Fairness tested? • Bias mitigation in place? 4. Transparency: • Explainability documented? • Consent management system? • Privacy by design? 5. Cross-Border: • Data sovereignty compliance? • Transfer agreements (SCCs/BCRs)? Each region has different requirements.  Build for the strictest, adapt for the rest. Which regulation applies to your GenAI app?

Interview Conversation Role: RTE in #SAFe Framework Topic: Preparation for PI Planning 👴 Interviewer : "PI Planning is around the corner. How would you ensure it's well-prepared and smooth for everyone involved?" 🧑 Candidate: "I’d make sure the teams know the agenda and are clear on their tasks." 👴 Interviewer: "Let’s add a layer. Imagine stakeholders have conflicting priorities, and teams are feeling unclear on dependencies. A lack of alignment could derail the PI. How would you structure your prep to tackle these challenges?" 🧑 Candidate: "I’d send out reminders about the PI objectives and ask teams to review their backlogs." What an effective Release Train Engineer should say: ---------------------------------------------------------- ✨ PI Planning success starts with comprehensive prep. I’d first facilitate a Pre-PI alignment workshop with Product Management, System Architects, and key stakeholders to clarify objectives and identify any competing priorities. This helps shape a single, clear vision for the PI. 💬 I’d then work closely with Product Owners and Scrum Masters to conduct a ‘Feature Readiness’ session to ensure all feature backlogs are prepared, dependencies mapped, and objectives aligned with our business goals. ✔ For example, in a previous ART, we held cross-team syncs in the lead-up week to discuss shared dependencies, which prevented delays and miscommunication during PI. 📊 Additionally, I’d ensure that the Solution Train and System Architects host architecture readiness sessions, providing teams with the necessary technical context. Any major risks or unknowns are surfaced early, allowing us to address them in a risk management session before PI Day. 🏹 Impact: With thorough preparation, everyone enters PI Planning focused, equipped, and aligned. This approach mitigates last-minute roadblocks, clarifies dependencies, and ensures that the ART can plan realistically, setting us up for a successful PI execution.

TIPS FOR YOUR INTERNSHIPS AND NEW GRAD POSITIONS - 2024 EDITION There are some things you learn better once the roles are reversed: I learned the importance of a good pull request (PR) once I started reviewing them myself. Here is a checklist you can refer to: 1. Getting your work reviewed doesn't shift the responsibility of catching issues to your reviewers. The prime responsibility of ensuring your work is defect-free and won't cause problems in prod is always on you, the author. The code review process is a guardrail, but don't treat it as a crutch: 'I'll have a senior engineer review my work, so I don't have to worry about testing the edge cases, they'll catch those.' This is the wrong mentality to create a PR with. If all your PRs involve reviewers pointing out edge cases, you're not doing your job diligently. 2. Document your PRs properly. Provide context, and don't take this for granted. Just because someone reviews your PR doesn't mean they'll have the complete background. Include the WHAT, WHY, and HOW. WHAT: Provide background on the issue. Example: this PR fixes an uncaught exception (include details about the exception). WHY - Why is this fix necessary? Example: the fix is needed because it helps prevent the app from crashing unexpectedly because of the uncaught exception. HOW - Example: It's fixed by encapsulating this block of code within a try-catch and logging the error. 3. Add instructions on how to reproduce the error and verify the fix locally. 4. For UI changes, including screenshots of before and after can be helpful. 5. Add tests! You'd be surprised how often this step is forgotten. 6. Keep the PR small, so it's easy to review. The usual guideline is less than 250 lines of code per PR. If it's too large, break it down into multiple PRs. 7. Review it first yourself. You'd be surprised by how many print statements you'll find that you forgot to clean up. 8. Assign the right reviewers. 9. Call out things you want to bring specific attention to, and you can cc specific people. 10. You don't have to necessarily agree with every piece of feedback provided - if there's something you feel strongly about, feel free to discuss it. If the discussion is getting too long, consider switching to a different medium - my goto is to jump on a quick call. 11. Give people enough time to review large PRs. If you're planning on merging a big feature on Friday afternoon (which isn't a great idea to begin with), don't create the PR on Thursday evening. There can be exceptions to this rule, but rushed reviews should be avoided. In the worst case, keep in mind that your PR could be reverted, which is why keeping the PR detailed is necessary. Got any more suggestions? Drop them in the comments below! #softwareengineering #technology

🚀 𝐉𝐮𝐬𝐭-𝐢𝐧-𝐭𝐢𝐦𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞 Ever tried to swim upstream while carrying 10 bricks? That’s what happens when we flood a project with documents long before anyone needs them. 🔎 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 We’ve all seen it. Documents are released way too early, requirements are still shifting, drawings are not stable, and work instructions are written before the process exists. Everything gets approved… and then reality hits. Design updates roll in, suppliers push new constraints, and interfaces change. Suddenly, you’re revising released documents again and again, burning change numbers and confusing everyone. Tip: Release documents just in time, when the downstream user actually needs them. Not earlier, not later. ✨ 𝐖𝐡𝐲 “𝐉𝐮𝐬𝐭-𝐢𝐧-𝐓𝐢𝐦𝐞” 𝐑𝐞𝐥𝐞𝐚𝐬𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 - Minimises waste: less time spent maintaining outdated docs. - Increases agility: documentation evolves with the product, not ahead of it. - Reduces risk: fewer chances that someone uses the “wrong” version. - Improves clarity & accountability: every release is a conscious, traceable event. 🛠️ 𝐇𝐨𝐰 𝐭𝐨 𝐝𝐨 “𝐉𝐮𝐬𝐭-𝐢𝐧-𝐓𝐢𝐦𝐞” 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞  1️⃣ Define release gates up front. In your CM plan, identify phases or triggers that justify a formal release, e.g., after the requirements freeze, module design sign-off, before procurement, pre-production, etc. CM2 promotes a dataset-based release approach rather than all-at-once or whenever you feel like it. 2️⃣ Release when downstream users need it. If procurement needs a long-lead item, release its documentation even if the full BOM isn’t ready. And yes, CM allows that. 3️⃣ Use a formal release mechanism with revision control. Every released document gets an identifier, a date, and a baseline reference, making it traceable. Once released, changes are controlled via a closed-loop change process. 4️⃣ Treat docs like parts: no “stockpiling.” Just as modern manufacturing embraces lean or Just-In-Time manufacturing to avoid excess inventory and waste, apply that lean logic to documentation, too. Only release what you need, when you need it. 5️⃣ Synchronize with actual workflows and avoid “fake readiness.” If documentation is released too early, teams may act on outdated or placeholder info. If released too late, it creates bottlenecks and risks rework. Use configuration-status accounting to track what’s released and what’s still draft. 🧩 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧 In a robust configuration management program, formal release isn’t a “one-and-done” event; it’s a rhythm. As the project matures, documents flow through baselines, but only when they are “needed and stable,” a CM2 Just-in-Time mindset. 🔁 So let’s drop the  “ready-all-docs-early” and “release-all-at-once” approaches and move to “release-on-demand.” #CM2 #ConfigurationManagement #PLM #ProductLifecycleManagement #Engineering #DocumentManagement #JustInTime #Lean #CM

GDPR & PDPA Compliance Testing isn’t just a checkbox — it’s your user’s trust at stake. When you build software that collects personal data, your testing strategy needs a serious upgrade. It’s not only about catching bugs anymore — it’s about preventing legal trouble and protecting real people. Test every data flow: how it's collected, stored, shared, and even deleted. Validate consent. Review access controls. Simulate breach scenarios. Ask yourself: can a user really delete their data? Can they access it on demand? Make privacy a feature, not a footnote. Involve legal teams early and treat requirements like product features. And most importantly, don’t wait for a complaint to test what should’ve been tested from day one. Compliance is not a final step — it’s baked into every release. #GDPR #PDPA #QualityAssurance #DataPrivacy #SoftwareTesting #QACommunity